Forecasting stage.
All the settings are shown in Table REF .
Following many recent works, such as MTGNN [1]} and GTS [2]}, we use the curriculum learning strategy for the forecasting task.
The strategy gradually increases the prediction length of the model with the increase in iteration number.
We increase the prediction length by one per cl_num epochs.
Moreover, we additionally perform a warm-up of warm_num epochs to better initialize the model for curriculum learning.
In addition, \(\lambda \)  in Equation (REF ) decays by \(\lambda =1/(\lceil epoch/6 \rceil )\) , where \(\lceil \cdot \rceil \)  means ceiling function and epoch is the epoch number.
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